Emergency clinical medicine and intensive care are areas where complex conditions present with overlapping clinical manifestations. Symptoms of several life-threatening conditions are often similar while the therapeutic options are diametrically opposite. Understanding the variability is crucial for quick initiation of appropriate therapy at the right time.
The vastness of medicine makes the recollection of all syndromes very difficult. In crucial situations, an algorithmic approach often helps to shortlist the differential diagnosis and prioritize the investigations and treatment. An algorithmic approach to medical emergencies is well known and documented. The literature available so far deals with such algorithms as standalone documents. However, quite often, the endpoints of one algorithm end in
clinical conditions which themselves need another algorithmic approach. It is therefore necessary for all algorithms to end seamlessly and lead to destination algorithms.
This initiative by the Hyderabad Intensive Care Symposium (HICS) team seeks to plug in a gap that exists in providing seamless algorithms. This collection of algorithms starts with a syndromic approach and leads to disease specific algorithms. These algorithms have been curated and edited by Senior Practitioners of intensive care who, with their years of teaching and work experience, have molded clinical science into a practical framework. This collection of algorithms will be of immense help in emergency rooms, high-dependency units, and intensive care units as well as any location or facility where patients with acute presentations are cared for.We hope that this publication will be well received and recommended.

The Hyderabad Intensive Care Symposium (HICS) team has been at the forefront of critical care academics for close to 18 years. The team has always focused on sharing information and bringing latest thoughts and concepts to the keen learners of intensive care. This book has been the long-cherished dream of the academic directors of the HICS team.